Optimization and Bayesian approaches to geoacoustic inversion

Simple item page
dc.contributor.authorLapinski, Anna-Liesa Salomeen_US
dc.date.accessioned2024-08-14T20:59:45Z
dc.date.available2024-08-14T20:59:45Z
dc.date.copyright2002en_US
dc.date.issued2002
dc.degree.departmentSchool of Earth and Ocean Sciences
dc.degree.levelMaster of Science M.Sc.en
dc.description.abstractThis thesis presents the results of applying two geoacoustic inversion meth­ods, an hybrid optimization algorithm and a Bayesian sampling algorithm, to data from environments with range independent and range dependent geophysical and geometric properties. The hybrid algorithm combines the local method of downhill simplex with the global method of simulated annealing, in an adaptive algorithm. The Bayesian inversion algorithm uses a Gibbs sampler to estimate properties of the posterior probabil­ity density (PPD), such as mean and maximum a posteriori parameter estimates, marginal probability distributions, highest posterior density in­tervals , and the model covariance matrix can be calculated. The methods were applied to noise-free and noisy benchmark data for several shallow ocean environments. An appropriate model parameterization is unknown for many of the environments, which increases the difficulty of the prob­lems. An under-parameterized approach was applied to determine the optimal parameterization. The model solutions were estimated well given the varying sensitivities of the parameters. The Bayesian inversion method provided complete solutions including quantitative uncertainty estimates to the inversion problems, while the hybrid inversion method provided parameter estimates in a fraction of the computation time.
dc.format.extent98 pages
dc.identifier.urihttps://hdl.handle.net/1828/18538
dc.rightsAvailable to the World Wide Weben_US
dc.titleOptimization and Bayesian approaches to geoacoustic inversionen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
LAPINSKI_Anna_Liesa_Salome_MSc_2002_1271636.pdf
Size:
29.72 MB
Format:
Adobe Portable Document Format
Download

Collections

Electronic Theses and Dissertations (ETD)
 
University of Victoria Libraries

We acknowledge and respect the Lək̓ʷəŋən (Songhees and Esquimalt) Peoples on whose territory the university stands, and the Lək̓ʷəŋən and W̱SÁNEĆ Peoples whose historical relationships with the land continue to this day.